Imaging Vision And Learning Based On Optimization And Pdes

Author : Xue-Cheng Tai
Publisher : Springer
ISBN 13 : 3319912747
Total Pages : 255 pages
Book Rating : 4.45/5 ( download)

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Download or read book Imaging, Vision and Learning Based on Optimization and PDEs written by Xue-Cheng Tai and published by Springer. This book was released on 2018-11-19 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs). It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.

Author : Sirakov Boyan
Publisher : World Scientific
ISBN 13 : 9813272899
Total Pages : 5396 pages
Book Rating : 4.97/5 ( download)

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Download or read book Proceedings Of The International Congress Of Mathematicians 2018 (Icm 2018) (In 4 Volumes) written by Sirakov Boyan and published by World Scientific. This book was released on 2019-02-27 with total page 5396 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Proceedings of the ICM publishes the talks, by invited speakers, at the conference organized by the International Mathematical Union every 4 years. It covers several areas of Mathematics and it includes the Fields Medal and Nevanlinna, Gauss and Leelavati Prizes and the Chern Medal laudatios.

Author : Ke Chen
Publisher : Springer Nature
ISBN 13 : 3030986616
Total Pages : 1981 pages
Book Rating : 4.12/5 ( download)

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Download or read book Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging written by Ke Chen and published by Springer Nature. This book was released on 2023-02-24 with total page 1981 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook gathers together the state of the art on mathematical models and algorithms for imaging and vision. Its emphasis lies on rigorous mathematical methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and nonlinear optimization, and provides the basis of high-resolution imaging through geometry and variational models. Besides, optimization naturally connects traditional model-driven approaches to the emerging data-driven approaches of machine and deep learning. No other framework can provide comparable accuracy and precision to imaging and vision. Written by leading researchers in imaging and vision, the chapters in this handbook all start with gentle introductions, which make this work accessible to graduate students. For newcomers to the field, the book provides a comprehensive and fast-track introduction to the content, to save time and get on with tackling new and emerging challenges. For researchers, exposure to the state of the art of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next decades of imaging and information services. This work can greatly benefit graduate students, researchers, and practitioners in imaging and vision; applied mathematicians; medical imagers; engineers; and computer scientists.

Author : Philipp Grohs
Publisher : Springer Nature
ISBN 13 : 3030313514
Total Pages : 701 pages
Book Rating : 4.17/5 ( download)

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Download or read book Handbook of Variational Methods for Nonlinear Geometric Data written by Philipp Grohs and published by Springer Nature. This book was released on 2020-04-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter is authored by leading experts in the respective discipline and provides an introduction, an overview and a description of the current state of the art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse and include, for instance, nonlinear spaces of matrices, spaces of curves, shapes as well as manifolds of probability measures. Applications can be found in biology, medicine, product engineering, geography and computer vision for instance. Variational methods on the other hand have evolved to being amongst the most powerful tools for applied mathematics. They involve techniques from various branches of mathematics such as statistics, modeling, optimization, numerical mathematics and analysis. The vast majority of research on variational methods, however, is focused on data in linear spaces. Variational methods for non-linear data is currently an emerging research topic. As a result, and since such methods involve various branches of mathematics, there is a plethora of different, recent approaches dealing with different aspects of variational methods for nonlinear geometric data. Research results are rather scattered and appear in journals of different mathematical communities. The main purpose of the book is to account for that by providing, for the first time, a comprehensive collection of different research directions and existing approaches in this context. It is organized in a way that leading researchers from the different fields provide an introductory overview of recent research directions in their respective discipline. As such, the book is a unique reference work for both newcomers in the field of variational methods for non-linear geometric data, as well as for established experts that aim at to exploit new research directions or collaborations. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com.

Author : Daniel Patel
Publisher : Springer Nature
ISBN 13 : 3030907163
Total Pages : 359 pages
Book Rating : 4.67/5 ( download)

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Download or read book Interactive Data Processing and 3D Visualization of the Solid Earth written by Daniel Patel and published by Springer Nature. This book was released on 2022-02-21 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents works detailing the application of processing and visualization techniques for analyzing the Earth’s subsurface. The topic of the book is interactive data processing and interactive 3D visualization techniques used on subsurface data. Interactive processing of data together with interactive visualization is a powerful combination which has in the recent years become possible due to hardware and algorithm advances in. The combination enables the user to perform interactive exploration and filtering of datasets while simultaneously visualizing the results so that insights can be made immediately. This makes it possible to quickly form hypotheses and draw conclusions. Case studies from the geosciences are not as often presented in the scientific visualization and computer graphics community as e.g., studies on medical, biological or chemical data. This book will give researchers in the field of visualization and computer graphics valuable insight into the open visualization challenges in the geosciences, and how certain problems are currently solved using domain specific processing and visualization techniques. Conversely, readers from the geosciences will gain valuable insight into relevant visualization and interactive processing techniques. Subsurface data has interesting characteristics such as its solid nature, large range of scales and high degree of uncertainty, which makes it challenging to visualize with standard methods. It is also noteworthy that parallel fields of research have taken place in geosciences and in computer graphics, with different terminology when it comes to representing geometry, describing terrains, interpolating data and (example-based) synthesis of data. The domains covered in this book are geology, digital terrains, seismic data, reservoir visualization and CO2 storage. The technologies covered are 3D visualization, visualization of large datasets, 3D modelling, machine learning, virtual reality, seismic interpretation and multidisciplinary collaboration. People within any of these domains and technologies are potential readers of the book.

Author : Abderrahim Elmoataz
Publisher : Springer Nature
ISBN 13 : 3030755495
Total Pages : 584 pages
Book Rating : 4.92/5 ( download)

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Download or read book Scale Space and Variational Methods in Computer Vision written by Abderrahim Elmoataz and published by Springer Nature. This book was released on 2021-04-29 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021, which took place during May 16-20, 2021. The conference was planned to take place in Cabourg, France, but changed to an online format due to the COVID-19 pandemic. The 45 papers included in this volume were carefully reviewed and selected from a total of 64 submissions. They were organized in topical sections named as follows: scale space and partial differential equations methods; flow, motion and registration; optimization theory and methods in imaging; machine learning in imaging; segmentation and labelling; restoration, reconstruction and interpolation; and inverse problems in imaging.

Author : Tobias Preusser
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681731444
Total Pages : 162 pages
Book Rating : 4.45/5 ( download)

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Download or read book Stochastic Partial Differential Equations for Computer Vision with Uncertain Data written by Tobias Preusser and published by Morgan & Claypool Publishers. This book was released on 2017-07-13 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties, should be—and more and more frequently are—taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate. The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations. The approach discussed in this book has the potential for application in all areas of quantitative computer vision, image processing, and image analysis. In particular, it might help medical imaging finally become a scientific discipline that is characterized by the classical paradigms of observation, measurement, and error awareness. This book is comprised of eight chapters. After an introduction to the goals of the book (Chapter 1), we present a brief review of PDEs and their numerical treatment (Chapter 2), PDE-based image processing (Chapter 3), and the numerics of stochastic PDEs (Chapter 4). We then proceed to define the concept of stochastic images (Chapter 5), describe how to accomplish image processing and computer vision with stochastic images (Chapter 6), and demonstrate the use of these principles for accomplishing sensitivity analysis (Chapter 7). Chapter 8 concludes the book and highlights new research topics for the future.

Author : S. Kevin Zhou
Publisher : Academic Press
ISBN 13 : 0128165863
Total Pages : 1074 pages
Book Rating : 4.67/5 ( download)

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Download or read book Handbook of Medical Image Computing and Computer Assisted Intervention written by S. Kevin Zhou and published by Academic Press. This book was released on 2019-10-18 with total page 1074 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention

Author : Marco Alexander Treiber
Publisher : Springer Science & Business Media
ISBN 13 : 1447152832
Total Pages : 266 pages
Book Rating : 4.35/5 ( download)

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Download or read book Optimization for Computer Vision written by Marco Alexander Treiber and published by Springer Science & Business Media. This book was released on 2013-07-12 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.

Author :
Publisher : Elsevier
ISBN 13 : 0444642064
Total Pages : 157 pages
Book Rating : 4.66/5 ( download)

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Download or read book Processing, Analyzing and Learning of Images, Shapes, and Forms: written by and published by Elsevier. This book was released on 2018-11-08 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Processing, Analyzing and Learning of Images, Shapes, and Forms: Volume 19, Part One provides a comprehensive survey of the contemporary developments related to the analysis and learning of images, shapes and forms. It covers mathematical models as well as fast computational techniques, and includes new chapters on Alternating diffusion: a geometric approach for sensor fusion, Shape Correspondence and Functional Maps, Geometric models for perception-based image processing, Decomposition schemes for nonconvex composite minimization: theory and applications, Low rank matrix recovery: algorithms and theory, Geometry and learning for deformation shape correspondence, and Factoring scene layout from monocular images in presence of occlusion. Presents a contemporary view on the topic, comprehensively covering the newest developments and content Provides a comprehensive survey of the contemporary developments related to the analysis and learning of images, shapes and forms